Explore global development with R

Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.

Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.

Get the necessary packages

First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.

## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──

## ✔ ggplot2 3.3.6     ✔ purrr   0.3.4
## ✔ tibble  3.1.7     ✔ dplyr   1.0.9
## ✔ tidyr   1.2.0     ✔ stringr 1.4.0
## ✔ readr   2.1.2     ✔ forcats 0.5.1

## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()

Look at the data and tackle the tasks

First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.

str(gapminder)

## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ year     : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ lifeExp  : num [1:1704] 28.8 30.3 32 34 36.1 ...
##  $ pop      : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
##  $ gdpPercap: num [1:1704] 779 821 853 836 740 ...

unique(gapminder$year)

##  [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007

head(gapminder)

## # A tibble: 6 × 6
##   country     continent  year lifeExp      pop gdpPercap
##   <fct>       <fct>     <int>   <dbl>    <int>     <dbl>
## 1 Afghanistan Asia       1952    28.8  8425333      779.
## 2 Afghanistan Asia       1957    30.3  9240934      821.
## 3 Afghanistan Asia       1962    32.0 10267083      853.
## 4 Afghanistan Asia       1967    34.0 11537966      836.
## 5 Afghanistan Asia       1972    36.1 13079460      740.
## 6 Afghanistan Asia       1977    38.4 14880372      786.

view(gapminder)

The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.

Let’s plot all the countries in 1952.

theme_set(theme_bw())  # set theme to white background for better visibility

ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

We see an interesting spread with an outlier to the right. Answer the following questions, please:

data_1952 <- gapminder %>% 
  filter(year == 1952)

min(data_1952$gdpPercap)

## [1] 298.8462

max(data_1952$gdpPercap)

## [1] 108382.4

richest <- match(c(max(data_1952$gdpPercap)), data_1952$gdpPercap)
richest

## [1] 72

data_1952$country[richest]

## [1] Kuwait
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe

Why does it make sense to have a log10 scale on x axis? > Because the numbers differ a lot (the smallest being 298.8462 and the biggest being 108382.4). The plot would be very difficult to read if we would try to visualise this.
Who is the outlier (the richest country in 1952 - far right on x axis)? > Kuwait

Next, you can generate a similar plot for 2007 and compare the differences

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()

…

The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.

Tasks:

Differentiate the continents by color, and fix the axis labels and units to be more legible (Hint: the 2.50e+08 is so called “scientific notation”, which you might want to eliminate)

# fixing the scientific notation
options(scipen=999)

ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(colour = factor(continent))) +
  labs (x = "GDP per Capita", y = "Life Expectancy") +
  scale_x_log10()

What are the five richest countries in the world in 2007?

data_2007 <-  gapminder %>% 
  filter(year == 2007)

top_5 <- tail(sort(data_2007$gdpPercap),5)
top_5

## [1] 40676.00 42951.65 47143.18 47306.99 49357.19

top_5_richest <- match(c(top_5), data_2007$gdpPercap)
top_5_richest

## [1]  63 135 114  72  96

data_2007$country[top_5_richest]

## [1] Ireland       United States Singapore     Kuwait        Norway       
## 142 Levels: Afghanistan Albania Algeria Angola Argentina Australia ... Zimbabwe

Ireland, United States, Singapore, Kuwait and Norway

Make it move!

The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. Beware that there may be other packages your operating system needs in order to glue interim images into an animation or video. Read the messages when installing the package.

Also, there are two ways of animating the gapminder ggplot.

Option 1: Animate using transition_states()

The first step is to create the object-to-be-animated

anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10()  # convert x to log scale
anim

…

This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the bottom right ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the visual inside an html file.

anim + transition_states(year, 
                      transition_length = 1,
                      state_length = 1)

…

Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.

Option 2 Animate using transition_time()

This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.

anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year)
anim2

The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.

Now, choose one of the animation options and get it to work. You may need to troubleshoot your installation of gganimate and other packages

Can you add a title to one or both of the animations above that will change in sync with the animation? (Hint: search labeling for transition_states() and transition_time() functions respectively)

anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year) +
  labs(title = 'Year: {frame_time}')
anim3

Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.

anim4 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point() +
  scale_x_log10() + # convert x to log scale
  transition_time(year) +
  labs(title = 'Year: {frame_time}',
       x = "GDP per Capita",
       y = "Life Expectancy")
anim4

Come up with a question you want to answer using the gapminder data and write it down. Then, create a data visualisation that answers the question and explain how your visualization answers the question. (Example: you wish to see what was mean life expectancy across the continents in the year you were born versus your parents’ birth years). [Hint: if you wish to have more data than is in the filtered gapminder, you can load either the gapminder_unfiltered dataset and download more at https://www.gapminder.org/data/ ]

How did the European countries’ GDP and life expectancy were in 1962 compared to the Americas?

data_1962 <-  gapminder %>% 
  filter(year == 1962 & continent=="Europe" | year == 1962 & continent=="Americas")

gdp_1962 <- ggplot(data_1962, aes(gdpPercap, lifeExp, size = pop)) +
  geom_point(aes(colour = factor(continent))) +
  scale_x_log10()  # convert x to log scale
gdp_1962

> On this graph, we can see that Europe had a better GDP status and life expectancy overall than America, who has more scattered datapoints.

HW5 - Make Data Move

Juli Furjes

05/10/2020